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The eye is one of the organs of the human senses, namely the sense of sight. Eyes has an important function in capturing visual information that is used for daily activities. Eye health is important because vision can't be replaced by anything.
Previously, a doctor made a diagnosis of an eye disease using retinal fundus images. But it takes expertise and a long time. Therefore, a classification system was made using the Convolutional Neural Network (CNN). The CNN network is used to recognize the visual pattern of image pixels with minimal preprocessing.
The variables used during testing are data and batch size for the CNN training process. The data variables consist of 50 images from each class which are reproduced using mirroring with a total of 1,000 images; 50 images from each class reproduced using rotation totaling 2,000 images; and 275 normal images, 55 diabetic images, 250 glaucoma images, 250 cataract images, and 170 hypertension images totaling 1,000 images. Batch size variables used were 25 and 32.
After all models were tested, it was concluded that the model trained using 1,600 images and 32 batch size gave the best results, namely loss: 0.1228 and accuracy: 0.9100.
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